Felipe Giuste
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Machine Learning in Healthcare 6
- Explainable Artificial Intelligence (XAI) 3
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- COVID-19 diagnosis using AI 9
- Co-authors
- May D. Wang (25 shared papers)Wenqi Shi (18 shared papers)Yuanda Zhu (12 shared papers)Monica Isgut (6 shared papers)Tong Li (5 shared papers)Gökhan Karakülah (2 shared papers)Vijender Chaitankar (2 shared papers)Matthew J. Brooks (2 shared papers)
- Journals
- Journal of Clinical Oncology (4 papers)Scientific Reports (3 papers)IEEE Reviews in Biomedical Engineering (3 papers)Blood (1 paper)IEEE Open Journal of Engineering in Medicine and Biology (1 paper)
- Partner nations
- United StatesAustriaSwitzerland
In The Last Decade
Felipe Giuste
36 papers receiving 585 citations
Peers
Comparison fields: 5 of 96
- Health Informatics 66
- Health Information Management 28
- Radiology, Nuclear Medicine and Imaging 76
- Biophysics 21
- Molecular Biology 223
Countries citing papers authored by Felipe Giuste
This map shows the geographic impact of Felipe Giuste's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Felipe Giuste with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felipe Giuste more than expected).
Fields of papers citing papers by Felipe Giuste
This network shows the impact of papers produced by Felipe Giuste. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Felipe Giuste. The network helps show where Felipe Giuste may publish in the future.
Co-authors
The 25 scholars most cited alongside Felipe Giuste, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 38 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2016 | 96 | |
| 2 | 2022 | 95 | |
| 3 | 2016 | 61 | |
| 4 | 2023 | 56 | |
| 5 | 2012 | 43 | |
| 6 | 2018 | 29 | |
| 7 | 2020 | 25 | |
| 8 | 2023 | 15 | |
| 9 | 2023 | 14 | |
| 10 | 2021 | 14 | |
| 11 | 2019 | 14 | |
| 12 | 2021 | 11 | |
| 13 | 2022 | 11 | |
| 14 | 2022 | 10 | |
| 15 | 2025 | 9 | |
| 16 | 2022 | 9 | |
| 17 | 2022 | 8 | |
| 18 | 2019 | 8 | |
| 19 | 2023 | 7 | |
| 20 | 2021 | 7 |
About Felipe Giuste
Felipe Giuste is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Molecular Biology, Surgery and Oncology, having authored 38 papers that have together received 600 indexed citations. Recurring topics across this work include COVID-19 diagnosis using AI (9 papers), Machine Learning in Healthcare (6 papers), Artificial Intelligence in Healthcare and Education (6 papers), Cancer Immunotherapy and Biomarkers (4 papers), Transplantation: Methods and Outcomes (4 papers), Cutaneous Melanoma Detection and Management (3 papers), Acute Ischemic Stroke Management (3 papers) and Explainable Artificial Intelligence (XAI) (3 papers). The work is most often cited by research in Health Informatics (66 citations), Health Information Management (28 citations), Radiology, Nuclear Medicine and Imaging (76 citations), Biophysics (21 citations) and Molecular Biology (223 citations). Felipe Giuste has collaborated with scholars based in United States, Austria and Switzerland. Frequent co-authors include May D. Wang, Wenqi Shi, Yuanda Zhu, Monica Isgut, Tong Li, Gökhan Karakülah, Vijender Chaitankar, Matthew J. Brooks, Anand Swaroop and Ying Sha. Their work appears in journals such as Journal of Clinical Oncology, Scientific Reports, IEEE Reviews in Biomedical Engineering, Blood and IEEE Open Journal of Engineering in Medicine and Biology.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.